Lattice methods for spectral estimation

Lattice forms provide convenient parametrization of rational spectra of stationary processes. A comprehensive summary of lattice algorithms for estimating spectral parameters of AR, MA, and ARMA processes is presented. It is shown that various well-known spectral estimation techniques, such as the Maximum Entropy Method (MEM) and Maximum Likelihood Method (MLM), can be efficiently computed from lattice parameters. Algorithms are presented for the autocorrelation, pre-windowed, and covariance methods of forming the sample covariance matrix.     

A comparison framework for breathing motion estimation methods from 4-D imaging

Motion estimation is an important issue in radiation therapy of moving organs. In particular, motion estimates from 4-D imaging can be used to compute the distribution of an absorbed dose during the therapeutic irradiation. We propose a strategy and criteria incorporating spatiotemporal information to evaluate the accuracy of model-based methods capturing breathing motion from 4-D CT images. This evaluation relies on the identification and tracking of landmarks on the 4-D CT images by medical experts. Three different experts selected more than 500 landmarks within 4-D CT images of lungs for three patients. Landmark tracking was performed at four instants of the expiration phase. Two metrics are proposed to evaluate the tracking performance of motion-estimation models. The first metric cumulates over the four instants the errors on landmark location. The second metric integrates the error over a time interval according to an a priori breathing model for the landmark spatiotemporal trajectory. This latter metric better takes into account the dynamics of the motion. A second aim of this paper is to estimate the impact of considering several phases of the respiratory cycle as compared to using only the extreme phases (end-inspiration and end-expiration). The accuracy of three motion estimation models (two image registration-based methods and a biomechanical method) is compared through the proposed metrics and statistical tools. This paper points out the interest of taking into account more frames for reliably tracking the respiratory motion.     

基于最小能量准则的参数估计圆周SAR三维成像方法

针对圆周合成孔径雷达成像模式下应用参数化估计的三维成像算法效率低、精度差的问题,提出一种基于最小能量准则的参数估计圆周SAR三维成像算法。该算法首先对成像场景进行粗略网格划分,利用参数估计的方法得到目标的粗略位置,其次利用最小能量准则和精细化网格的方法得到目标精确三维位置和散射强度系数,最后通过CLEAN技术消除已估计点带来的影响,实现场景的三维成像。仿真实验结果表明:所提成像方法能有效对圆周观测下目标进行三维成像,同时与传统算法相比,解决了传统算法效率低、对目标估计不准确的问题,验证了所提算法的有效性。     

Power spectral density estimation via wavelet decomposition

A soft decision algorithm for wavelet decomposition, in which a probability measure is assigned to each frequency band bearing energy, is presented. This soft decision algorithm is used as an approximate estimator of power spectral density. A staircase approximation of power spectral density (PSD) is obtained by plotting the 2/sup m/ probabilities after an m-stage decomposition. Different wavelet filters are used for estimating the PSD of a speech segment. The type of the wavelet filter used can be selected as a compromise between accuracy and complexity.     

SAR成像中动目标的速度估算

提出了一种在SAR成像中估算动目标速度的方案.由于动目标的速度由距离向和方位向两个速度构成,因此它的估算也分为两个方面.首先是距离向速度的估算,由杂波锁定得到多普勒中心频率,再由多普勒中心频率估算出距离向速度.其次是方位向速度的估算,由自聚焦得到多普勒调频率,再由多普勒调频率得到方位向速度.最后将两个速度合成便能得到该动目标的速度.文中的仿真结果证明了这种方案的有效性.     

The application of spectral estimation methods to bearing estimation problems

The equivalence between the problem of determining the bearing of a radiating source with an array of sensors and the problem of estimating the spectrum of a signal is demonstrated. Modern spectral estimation algorithms are derived within the context of array processing using an algebraic approach. Emphasis is placed on the problem of determining the bearing of a sound source with an array. Special issues encountered in applying these estimates are discussed.     

New algorithm for solving block matrix equations with applicationsin 2-D AR spectral estimation

The author presents an iterative algorithm to solve Toeplitz and non-Toeplitz block matrix equations. The development is based upon some well-known matrix iterative techniques. The algorithm is developed for the ideal case where the individual blocks in the autocorrelation matrix are Toeplitz, and it is then extended to a more general least squares data formulation case. It is shown that the algorithm requires fewer computations than the direct matrix inversion methods and is very simple to implement. The algorithm is applied to compute the spectral estimates of 2-D data of very small size based on the least squares data formulation with a quarter plane support     

Efficient 2-D DOA and frequency estimation for L-shaped array via RD-PM

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach.     

SAR stereo imaging algorithm based on Capon spectrum estimation

This letter presents a novel approach for the Synthetic Aperture Radar （SAR） stereo imaging based on the Capon spectrum estimation technique. In order to deal with nonuniform sampling space and lead to super resolution in the elevation direction, Capon approach is used to focus the SAR data on a certain height. Results obtained on simulated data demonstrate the feasibility of the Capon based algorithm. Compared with the classical Fast Fourier Transform （FFT）, the Capon based algorithm shows better resolution quality.     

Smoothed nonparametric spectral estimation via cepsturm thresholding - Introduction of a method for smoothed nonparametric spectral estimation

Cepstrum thresholding is shown to be an effective, automatic way of obtaining a smoothed nonparametric estimate of the spectrum of a stationary signal. In the process of introducing the cepstrum thresholding-based spectral estimator, we discuss a number of results on the cepstrum of a stationary signal, which might also be of interest to researchers in spectral analysis and allied topics, such as speech processing     

二维匹配滤波实现多角度SAR成像

 多角度SAR图像能够更全面描述目标不同角度下的特征信息,对目标识别具有重要意义.实现多角度SAR成像需要解决两个问题,一是在多角度测量模式下,传统成像算法的远场条件不能满足,成像散焦严重;二是空间采样不连续使得基于傅里叶变换的成像算法产生很高的旁瓣.本文利用二维匹配滤波函数的聚焦功能实现多角度SAR成像.通过调整成像参考点位置构造二维匹配函数,然后将测量数据用匹配函数进行滤波.与传统SAR成像算法相比,本文提出的多角度SAR成像算法突破了空间采样必须均匀和连续的束缚,更具有普适性.实验结果表明本文算法不仅能够实现多角度SAR成像,提高成像分辨率,而且多角度SAR图像能够描述目标散射特征的空间变化.     

基于二维提升结构的SAR相干斑噪声抑制

针对SAR图像提出了一种新的二维提升变换斑点噪声抑制方法.这种方法利用提升结构构造的双正交小波所具有的良好的逼近和重构性能,以及平滑面区域的特性而实现.仿真时将阈值去噪环节加入变换中.仿真结果表明,本文提出的二维提升变换方法在充分抑制图像斑点噪声的同时,能很好地保持均匀区域内的辐射特性,图像中的边缘、细小特征和点目标,以及图像的纹理特征,克服了传统降噪方法的不足,具有较好的视觉效果.     

Closed-form 2-D angle estimation with rectangular arrays in elementspace or beamspace via unitary ESPRIT

The UCA-ESPRIT is a closed-form algorithm developed for use in conjunction with a uniform circular array (UCA) that provides automatically paired source azimuth and elevation angle estimates. The 2-D unitary ESPRIT is presented as an algorithm providing the same capabilities for a uniform rectangular array (URA). In the final stage of the algorithm, the real and imaginary parts of the ith eigenvalue of a matrix are one-to-one related to the respective direction cosines of the ith source relative to the two major array axes. The 2-D unitary ESPRIT offers a number of advantages over other proposed ESPRIT based closed-form 2-D angle estimation techniques. First, except for the final eigenvalue decomposition of a dimension equal to the number of sources, it is efficiently formulated in terms of real-valued computation throughout. Second, it is amenable to efficient beamspace implementations that are presented. Third, it is applicable to array configurations that do not exhibit identical subarrays, e.g., two orthogonal linear arrays. Finally, the 2-D unitary ESPRIT easily handles sources having one member of the spatial frequency coordinate pair in common. Simulation results are presented verifying the efficacy of the method     

Noninvasive estimation of tissue temperature via high-resolution spectral analysis techniques

We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).     

基于非圆信号的二维测向算法

提出了一种基于窄带非圆信号的二维测向算法,利用非圆信号的椭圆协方差矩阵非零特性将阵列有效阵元数和孔径同时扩展,提高多信号检测的能力。进一步通过利用L阵的几何性质和两子阵间的互相关来滤除噪声,提高角度估计精度,并采用快速算法获得信号方位角和俯仰角。最后计算机仿真结果验证了该方法由于利用非圆信号特性比传统测向算法的估计性能更高。     

2种多通道SAR/GMTI相位误差估计方法

针对多通道合成孔径雷达(SAR)和地面运动目标检测(GMTI)系统,提出了2种通道相位误差估计方法:a)利用杂波的信号特征向量与其导向矢量的线性关系直接进行通道误差估计;b)通过对回波数据进行方位重采样,然后利用杂波信号特征向量张成的空间(即信号子空间)与真实导向矢量张成的空间相同的原理进行误差估计。实验证明,2种方法均能有效地进行通道相位误差估计,并且方法 b)具有更高的估计精确度。     

Weighting window optimization for classical 2-D spectral analysis

This article proposes a cost minimization approach to the problem of optimizing weighting digital windows used in classical 2-D spectral analysis. We suggest a general criterion form characterizing the 2-D spectral spreading caused by a window. Then this criterion is minimized and this results in an optimal shape of the window, extending an approach given by A. Papoulis in the case of monodimensional analog signals. We focus mainly on the minimization of a modification of the second order moment adapted to sampled signals. This leads to a natural extension of the optimal window of Papoulis to the case of 2-D digital signals. In the case of rectangular sampling, this optimal window is separable and has a simple analytical form. It is the product of two 1-D half period long cosine lobe windows. Results are also given in the case of hexagonal sampling. Other possible forms of the cost function are also discussed.     

合成孔径雷达二维余弦调相转发干扰研究

该文提出一种新的SAR干扰方法：二维余弦调相转发干扰。该方法通过转发调制了余弦相位的SAR信号,使得SAR回波在距离向频域和方位向多普勒域成对扩展,实现了对SAR的多假目标干扰。理论研究表明,该干扰样式为二维相干干扰,能量利用率高,同时又具有非相干干扰的压制特点,通过优化设置干扰调制参数,可以形成紧凑的面假目标或网格状假目标串,达到掩护分布式目标的目的。仿真实验和基于实测数据的仿真验证了该方法的有效性。     

2-D phase unwrapping and instantaneous frequency estimation

The phase of complex signals is wrapped since it can only be measured modulo-2π; unwrapping searches for the 2π-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Given an estimate of the phase gradient (i.e., of the instantaneous frequency), the 2-D unwrapped phase can be obtained as a solution of a variational problem. The analysis of unwrapping is done quite separately from instantaneous frequency estimation so that the reliability of both steps can be assessed independently. Various methods for evaluating 2-D instantaneous frequency are presented and compared in the presence of noise and amplitude variations. A study has also been made on aliasing arising in areas where, with respect to instantaneous frequency, spatial sampling is insufficient. The presence of noise in the data further complicates phase aliasing analysis since there is no way to distinguish between the aliasing due to noise or that due to steep phase slopes